BayesianNetworks

This video will be improved towards the end, but it introduces bayesian networks and inference on BNs. On the first example of probability calculations, I said Mary does not call, but I went through the example as if she had called (m=True). Sorry if that caused any confusion.

[@fiacobelli]

YES, many of you noticed... The exercise assumes that both John and Mary called. Sorry about that. if Mary hadn't called, I should have used m=f throughout.

[@Ray-eb5cy]

I was scared for a second cause I thought I really understood up until then. I have a question though, let's say we weren't concerned about John nor Mary and we simply wanted to know the probability of P(B| E, A) do we still need to compute the probabilities involving John and Mary or would we apply variable elimination to remove them?

[@rasikapurohit]

truly I wasted my tuition fee where a robot explained me to this. Thanks a ton!

[@user-rf4vc7mt4d]

im studying this before our prof started this topic. i have a project on it and i just know im not gonna learn anything from him

[@mireazma]

Thanks for the clear explanations. I finally understand how to calculate probabilities in bayesian networks. I hope you don't mind but please use a consistent capitalization.

[@moonman239]

If it helps, consider a simpler example:
Suppose John and Mary have instead agreed to watch your house and then call you if they see that someone has broken in. If someone breaks in, John has a 70% chance of calling you, but he also has a 40% chance of calling you if a door-to-door salesman shows up to your door. If someone breaks in, Mary will have a 60% chance of calling you, but a 30% chance of reporting a door-to-door salesman. The probability of someone breaking into your house is 0.1% and the probability of a door-to-door salesman ringing your door is 10%.
Both people will only call if they think you've been burgled, but we don't know when, only that they will call within a given amount of time after the person is spotted.
First John calls you, so the probability of the call being due to a burglar is the probability of there being a burglar (0.1%), times the probability of John calling given there's a burglar (70%), divided by the probability of John calling you (which is the probability of John calling given there's a burglar + the probability of John calling given there's a salesman), or 0.1% * 70% / (0.1% * 70% + 10% * 40%) = 1.7%.
Then Mary calls, and the probability you've actually been burgled is 1.7% * 60% / (1.7% * 60% + 10% * 30%) = 25%

[@RoastedTuberLp]

Holy shit thanks dude, you explain it in such an understandable way!

[@Claxiux]

thank you for your help, these videos are very valuable!

[@iamtechboy3298]

WOW! Super amazing way. For this first time i'm knowing what that hack really Bayesian Networks is. Thanks, Long Live. Hats Off.

[@navjotkaurbhamrah8233]

This video really helped me. Thank you !!

[@bakarandguladze]

Thank you, mate! very helpful

[@Igglesnoop]

Excellent explanation.

[@meerageorge9656]

Here at 14:02, Mary doesn't call is given in question. My understanding is that we have to evaluate for m=f then but it is taken as m=t. Kindly clarify the case.

[@mireazma]

He actually talks about John calling and Mary not calling, on two occasions, both in which it's written m=t.
I'm guessing the probability that we should evaluate with m=f instead of m = t, is 0.9 :)

[@tuongnguyen9391]

wow thank you so much, but what playlist of yours does this video belong to ?
I am really feel grateful

[@Igor-rd2fb]

4:40 How did they calculate P(A|B,E)?

[@usmanabbas7]

Thank you so much Sir :)

[@mahnooraftab2220]

Hi, regarding to the conditional independence, we say that J and M are independent given A. But my question is that J and M are independent given B?
Help will be really appreciated.
Great video.

[@joeleepee]

Great explanation!would u teach us about learning of bayesian network?TKS!

[@ifyonye6842]

Can i get your email to discuss a challenge i have with Bayesian Network?

[@taggebagge]

The usage of the names Mary and John will forever be ruined in my mind due to the video with that Indian fellow who explains the various usages of the word "fuck". Great vid though, thankss.

[@AyushMo]

haha, curious which video you're referring to(although yes this was a year ago so I know I'm pushing my luck in expecting a reply but meh)

[@taggebagge]

@@AyushMo Nah, you are certainly not pushing your luck in this regard, this one it is, all the best man:
th-cam.com/video/Bdgi6PAtH1Q/w-d-xo.html

[@AyushMo]

@@taggebagge Haha damn, thank you so much

[@taggebagge]

@@AyushMo You are most welcome.

[@qamariqbal2018]

I think I should have a PhD in math and then find out how should I respond to this alarm. I will let burglar to take away everything and leave me alone

[@thanhnguyenquang1513]

the sound is not good I am quite disappointed